FAQ
What is the difference between supervised and unsupervised learning in AI?
In AI, supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct output. The model learns to map inputs to outputs by minimizing the difference between its predictions and the actual labels. In contrast, unsupervised learning deals with unlabeled data, where the model tries to identify patterns, relationships, or structures within the data without predefined labels. Common unsupervised learning techniques include clustering and dimensionality reduction.
How do neural networks mimic the human brain?
Neural networks are inspired by the structure and function of the human brain. They consist of layers of interconnected nodes, or “neurons,” that process input data. Each neuron receives input, applies a weight to it, sums the result, and passes it through an activation function to determine the output. This process is akin to how neurons in the brain transmit signals. By adjusting the weights through training, neural networks learn to recognize patterns and make decisions, similar to how the brain learns from experience.